Single population mean, unknown standard deviation, student's-t distribution
Use the Student's-t Distribution with degrees of freedom
.
Single population proportion, normal distribution
Use the Normal Distribution for a single population proportion
The confidence interval has the format
.
Point estimates
is a point estimate for
is a point estimate for
is a point estimate for
Critical values
is a critical value, based on the Normal Curve for an exact confidence interval such as for a 95% confidence interval.
is a critical value, based on the Student's-t for an exact confidence interval.
Glossary
Binomial distribution
A discrete random variable (RV) which arises from Bernoulli trials. There are a fixed number,
, of independent trials. “Independent” means that the result of any trial (for example, trial 1) does not affect the results of the following trials, and all trials are conducted under the same conditions. Under these circumstances the binomial RV
is defined as the number of successes in
trials. The notation is:
~
. The mean is
and the standard deviation is
. The probability of exactly
successes in
trials is
.
Confidence interval (ci)
An interval estimate for an unknown population parameter. This depends on:
(1). The desired confidence level.
(2). Information that is known about the distribution (for example, known standard deviation).
(3). The sample and its size.
Confidence level
The percent expression for the probability that the confidence interval contains the true population parameter. For example, if the CL=90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter.
Degrees of freedom (df)
The number of objects in a sample that are free to vary.
Margin of error for a population mean (ebm)
The margin of error. Depends on the confidence level, sample size, and known or estimated population standard deviation.
Margin of error for a population proportion (ebp)
The margin of error. Depends on the confidence level, sample size, and the estimated (from the sample) proportion of successes.
Inferential statistics
Also called statistical inference or inductive statistics. This facet of statistics deals with estimating a population parameter based on a sample statistic. For example, if 4 out of the 100 calculators sampled are defective we might infer that 4 percent of the production is defective
Normal distribution
A continuous random variable (RV) with pdf
, where
is the mean of the distribution and
is the standard deviation. Notation:
~
. If
and
, the RV is called
the standard normal distribution .
Parameter
A numerical characteristic of the population.
Point estimate
A single number computed from a sample and used to estimate a population parameter.
Student-t distribution
Investigated and reported by William S. Gossett in 1908 and published under the pseudonym Student. The major characteristics of the random variable (RV) are:
(1). It is continuous and assumes any real values.
(2). The pdf is symmetrical about its mean of zero. However, it is more spread out and flatter at the apex than the normal distribution.
(3). It approaches the standard normal distribution as n gets larger.
(4). There is a "family" of t distributions: every representative of the family is completely defined by the number of degrees of freedom which is one less than the number of data
Standard deviation
A number that is equal to the square root of the variance and measures how far data values are from their mean. Notation: s for sample standard deviation and σ for population standard deviation.
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Source:
OpenStax, Collaborative statistics using spreadsheets. OpenStax CNX. Jan 05, 2016 Download for free at http://legacy.cnx.org/content/col11521/1.23
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